4 AI Imperatives for Higher Education in 2024
How will artificial intelligence impact colleges and universities this year? We asked AI and higher education leaders for their predictions and thoughts on the most important issues to consider as the technology evolves and adoption expands. Here's what they told us.
1) Responsible AI Will Be Critical as Complex Issues Persist
In 2024, AI in education will continue evolving with new architectures and transformer models like GPT-5 and Gemini 2 leading the way. However, the complexity of developing robust GenAI solutions might slow the adoption of open source models in ed tech. Institutions will navigate between adopting AI assistants within existing applications, leveraging GUI-based low-code platforms, utilizing API connections to proprietary models, and building custom stacks for enhanced privacy.
The most crucial considerations for education institutions will be the accessibility and ethical implementation of these technologies. Adoption will vary, with AI assistants and low-code platforms likely gaining traction for their ease of integration and user-friendliness.
However, the need for data privacy and custom solutions might drive some toward locally hosted models or API-based connections to sophisticated services. Regardless of the approach, integrating responsible AI practices — like improving source attribution, debiasing datasets, and ensuring privacy — will be vital. These measures are not just ethical imperatives but also crucial for maintaining trust and efficacy in educational environments.
As we move into 2024, education institutions must weigh the promise of AI against these practical and ethical considerations, ensuring that the technology they adopt not only enhances learning but also aligns with the core values of education.
— Noble Ackerson, CTO, American Board of Design and Research
Higher education will continue to engage some very complex and unresolved questions that generative AI raises. There are legal questions pertaining to intellectual property, not only in terms of how generative AI models were trained but also, and perhaps more importantly for research and other creative activities, how the use of generative AI may impact the ownership of the products of our work. It will also be paramount for educational institutions to think deeply about the issues around both bias and inaccuracies that emerge from how generative AI's models are developed and how they work to produce output. In all of our disciplines and professions, we are still coming to terms with what responsible use of generative AI is, especially in ways that empower people as decision-making agents.
— Trey Conatser, Ph.D., director, Center for the Enhancement of Learning and Teaching, University of Kentucky
2) Institutions Must Take AI Skills Training Seriously
There's a crying need for faculty and staff professional development about generative AI. The topic is complicated and fast moving. Already the people I know who are seriously offering such support are massively overscheduled. Digital materials are popular. Books are lagging but will gradually surface. I hope we see more academics lead more professional development offerings.
For an academic institution to take emerging AI seriously it might have to set up a new body. Present organizational nodes are not necessarily a good fit. For example, a computer science department can be of great help in explaining the technology, but might not have a lot of experiencing in supporting non-CS teaching with AI. Campus IT will probably be overwhelmed already, and might not have the academic cloud needed to win the attention of some faculty and staff. Perhaps a committee or team is a better idea, with members drawn from a heterogeneous mix of the community. Not to be too alarmist, but we might learn from how some institutions set up emergency committees to handle COVID in 2020, bringing together diverse subject matter experts, stakeholders, and operational leaders. If a campus population comes to see AI as a serious threat, this might be a useful model.